Epstein Secrets

twork, in which a hierarchy exists, along with a rich network of heterarchical links joining nodes in t

hich data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "adva

ilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, l

etwork, in which a hierarchy exists, along with a rich network of heterarchical links joining nodes in t

mpilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, lea

which data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "advan

ImplicationLink_HOJ Inh SX stupid <.9> Inh SX rich <.97 EFTA00624176 30 20 Knowledge Representat

Agent migration, and machines joining or leaving the CogPrime cluster. So, at the Unit level, attention allocation in CogPrime has two aspects: how MindAgents and Tasks receive attention from CogPrime, and how Atoms receive attention from different MindAgents and Tasks. The topic of this Section is the fo

VDIFF. Step A calculates the amount by which the rich have been made poorer, and uses this to make the

idual cognitive processes. The CogPrime design gravitates more toward the latter approach. though also with some specific mechanisms within various MindAgents; and efforts have been made to have these specific mechanisms modulated by the generic attention allocation structures and dynamics wherever possibl

29.7 Conclusion 189 flexibly interacting with a rich stream of data from the everyday human world. Thi

to Atoms with more impor- tance, and hence creates an evolutionary, dynamic with importance as the fitness criterion and the whole constellation of MindAgents as the novelty-generation mechanism. However, MOSES explicitly embodies evolutionary dynamics for the learning of new patterns and procedures that

value query with target Al a InheritanceLink Bob rich Suppose the deduction rule It is chosen. Then i

links that could simply be found by the pattern matcher when trying to find variable assignments to satisfy (El, E2, E3, E4}. 36.4.1 Breakdown into MindAgents To make this sort of PLN dynamic work, we require a number of MindAgents to be operating "ambiently" in the background whenever inference is occurr

s. - We have not yet thought through exactly how rich the semantic formalism should be for handling th

sambigua- tion that needs to be done to get the structure of the resultant Atom-set correct. Any further disambiguation is left to be done later, by MindAgents acting on the Atom-sets after they've already been placed in the AtomSpace. • In the old approach, the RelEx2Frame rules attempted, in many cases,

g up a block in the dark is a perfect example of rich coupling of perception and action networks. Feeli

d effort management frameworks attempt to enforce this, via minimizing the amount of effort spent by the system in getting to a certain conclusion. MindAgents operating primarily on one kind of knowledge (e.g. MOSES, PLN) may for a time seek to follow the shortest paths within their particular correspondin

ine instances of matter into new ones in a fairly rich way: e.g. glue or tie solids togethermix liquids

gh STI., which rapidly decreases in meet cases local workspaces bubbles of interlinked Atoms with moderate impor- tance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory HebbianLinks in the AT sensory memory spaceserver/timeserve

ine instances of matter into new ones in a fairly rich way: e.g. glue or tie solids togethermix liquids

high STI, which rapidly decreases in most cases ocal workspaces bubbles of interlinked Atoms with moderate impor- ance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory |HebbianLinks in the AT sensory memory spaceserver /timeserver